Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/OpenDCAI/DataFlow-WebUInpx agentmods add skills/opendcai/dataflow-webui/generating-dataflow-pipelineWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/opendcai/dataflow-webui/generating-dataflow-pipeline)<a href="https://agentmods.dev/skills/opendcai/dataflow-webui/generating-dataflow-pipeline"><img src="https://agentmods.dev/badge/skills/opendcai/dataflow-webui/generating-dataflow-pipeline/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/opendcai/dataflow-webui/generating-dataflow-pipeline"><img src="https://agentmods.dev/badge/skills/opendcai/dataflow-webui/generating-dataflow-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 8 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 65 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- high Prompt Injection · line 72 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- high Prompt Injection · line 81 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- high Prompt Injection · line 188 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- high Prompt Injection · line 316 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- medium Data Exfiltration · line 151 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 161 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 311 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00055 | $0.06097 |
| Opus 5.5 | $0.00022 | $0.02439 |
| Sonnet 5.5 | $0.00011 | $0.01219 |
| Haiku 4.5 | $0.00006 | $0.00610 |
Grade A, and why
generating-dataflow-pipeline scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 455 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DataFlow Pipeline Code Generator
Goal
This skill is used when users provide:
- Target: What the pipeline should achieve
- Sample Data File: Path to a JSONL file containing 1-5 representative data samples
The skill must:
- Read and analyze the JSONL file at the provided path
- Infer data structure, field types, and content characteristics
- Determine task type based on file content (document processing, text transformation, multi-field composition)
- Select appropriate operators from preferred primitives
- Validate field dependencies
- Output intermediate operator decision summary
- Generate standard DataFlow pipeline code with
first_entry_file_nameset to the user-provided file path
User Input Format
Users provide:
Target: [Clear task description]
Sample file: [Path to JSONL file, e.g., ./data/input.jsonl]
Expected outputs: [Optional field list]
Important: The sample file is a JSONL file (one JSON object per line), not a JSON array.
Preferred Operator Strategy
Six Core Primitives (high-coverage operators for most data science tasks):
PromptedGenerator- Single-field LLM generationFormatStrPromptedGenerator- Multi-field template generationText2MultiHopQAGenerator- Multi-hop QA pair constructionPromptedFilter- LLM-based quality filteringGeneralFilter- Rule-based filtering- KBC trio (always used together in order):
FileOrURLToMarkdownConverterFlash→KBCChunkGenerator→KBCTextCleaner
These are preferred primitives, not fixed workflows. They can be used repeatedly and combined flexibly.
Operator Selection Priority Rule (MANDATORY)
When a specialized operator exists for the task, it MUST be used over generic operators. Do NOT use PromptedGenerator to replicate functionality that a dedicated operator already provides.
Decision table (check in order, use the first match):
| Task / Scenario | Required Operator | Do NOT use |
|---|---|---|
| Generate QA pairs from text | Text2MultiHopQAGenerator |
PromptedGenerator with QA prompt |
| Convert file path / URL to text | KBC trio (FileOrURLToMarkdownConverterFlash → KBCChunkGenerator → KBCTextCleaner) |
PromptedGenerator to summarize files |
| Score / evaluate using multiple fields | FormatStrPromptedGenerator + GeneralFilter |
PromptedFilter (single input_key only) |
| Filter by deterministic rule on existing fields | GeneralFilter |
PromptedFilter |
| Generate new content from a single field | PromptedGenerator |
— |
| Generate new content from multiple fields | FormatStrPromptedGenerator |
Multiple PromptedGenerator steps |
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 455 lines · 55 tokens per session scan A 69a2cd2d9cd3
generating-dataflow-pipeline is a skill published in the GitHub repository OpenDCAI/DataFlow-WebUI (253 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 55 tokens to every session and 6,097 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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